CASE STUDY
End-to-End Automated Pipeline for Data Analysis
Automated vendor data monitoring and comparison enabling faster insights, reduced manual effort, and self-service analytics.
Problem Solved
The client lacked an automated data pipeline solution for ingesting and comparing vendor datasets against production data, making manual analysis slow, error-prone, and inefficient, with no simple way to track or visualize changes and trends over time.
Problem Solving Approach
The solution was designed as a fully automated data pipeline built with a modern data engineering stack. The project focused on integrating automated ingestion, transformation, comparison, storage, and visualization workflows into a single streamlined process for vendor data analysis.
The approach combined Snowflake, Python, SQL, Apache Sedona, and Tableau to support automated data delivery detection, complex comparison queries against production datasets, downstream-ready result storage, and self-service visualization of changes through dynamic dashboards. All code, scripts, and YAML configurations were maintained within the client’s Git repository.
Outcome
This solution replaced potential months of manual effort with a fully automated, scalable system. It provides the client with a near real-time “single source of truth” for data quality and empowers stakeholders to self-serve insights through an intuitive dashboard.
Replaced potential months of manual effort with a fully automated, scalable system
Provided the client with a near real-time “single source of truth” for data quality
Empowered stakeholders to self-serve insights through an intuitive dashboard
Key Features Implemented
Automated Vendor Data Ingestion
Automatically detect and pull new vendor data deliveries from PostgreSQL into Snowflake using orchestrators with Python and Bash.
Data Transformation & Production Comparison
Use Snowflake SQL and Apache Sedona within Snowflake to transform datasets and execute complex comparison queries against production data.
Delivery Trend Analysis
Generate comparative reports between vendor deliveries, including Record Lifecycle, Attribute Change Analysis, and Impact Assessment metrics.
Intelligent Monitoring & Workflow Automation
Automation layer that detects new deliveries, preprocesses data, runs quality analysis, and executes Trend Analysis workflows automatically.
Change Visualization & Reporting
Dynamic Tableau dashboards providing a self-serve view of changes, including detailed metrics on name, attribute, and geometry changes using spatial comparison engines.
Centralized Storage & Version Control
Transformation, comparison, and analysis results stored in Snowflake and managed through the client’s Git repositories, including scripts and YAML configurations.
Technologies
Development
Project Timeline and Team Structure
The project was delivered in 2026 through Data Science and Analytics and Data Engineering services. It lasted 9 months and included 5 team members consisting of Data Analysts and Data Engineers, structured to support efficient collaboration and delivery throughout the project lifecycle.
Data Engineer
Data Analyst
Methodology
Project was delivered using the Agile Scrum methodology, enabling iterative development, continuous feedback, and close collaboration with the client’s internal teams.
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